import pandas as pd
import numpy as np
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
from pandas_profiling import ProfileReport
%matplotlib inline
#Uploading dataset
df=pd.read_csv('CarPrice_data.csv')
#Display Dataset
df
| car_ID | symboling | CarName | fueltype | aspiration | doornumber | carbody | drivewheel | enginelocation | wheelbase | ... | enginesize | fuelsystem | boreratio | stroke | compressionratio | horsepower | peakrpm | citympg | highwaympg | price | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 3 | alfa-romero giulia | gas | std | two | convertible | rwd | front | 88.6 | ... | 130 | mpfi | 3.47 | 2.68 | 9.0 | 111 | 5000 | 21 | 27 | 13495.0 |
| 1 | 2 | 3 | alfa-romero stelvio | gas | std | two | convertible | rwd | front | 88.6 | ... | 130 | mpfi | 3.47 | 2.68 | 9.0 | 111 | 5000 | 21 | 27 | 16500.0 |
| 2 | 3 | 1 | alfa-romero Quadrifoglio | gas | std | two | hatchback | rwd | front | 94.5 | ... | 152 | mpfi | 2.68 | 3.47 | 9.0 | 154 | 5000 | 19 | 26 | 16500.0 |
| 3 | 4 | 2 | audi 100 ls | gas | std | four | sedan | fwd | front | 99.8 | ... | 109 | mpfi | 3.19 | 3.40 | 10.0 | 102 | 5500 | 24 | 30 | 13950.0 |
| 4 | 5 | 2 | audi 100ls | gas | std | four | sedan | 4wd | front | 99.4 | ... | 136 | mpfi | 3.19 | 3.40 | 8.0 | 115 | 5500 | 18 | 22 | 17450.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 200 | 201 | -1 | volvo 145e (sw) | gas | std | four | sedan | rwd | front | 109.1 | ... | 141 | mpfi | 3.78 | 3.15 | 9.5 | 114 | 5400 | 23 | 28 | 16845.0 |
| 201 | 202 | -1 | volvo 144ea | gas | turbo | four | sedan | rwd | front | 109.1 | ... | 141 | mpfi | 3.78 | 3.15 | 8.7 | 160 | 5300 | 19 | 25 | 19045.0 |
| 202 | 203 | -1 | volvo 244dl | gas | std | four | sedan | rwd | front | 109.1 | ... | 173 | mpfi | 3.58 | 2.87 | 8.8 | 134 | 5500 | 18 | 23 | 21485.0 |
| 203 | 204 | -1 | volvo 246 | diesel | turbo | four | sedan | rwd | front | 109.1 | ... | 145 | idi | 3.01 | 3.40 | 23.0 | 106 | 4800 | 26 | 27 | 22470.0 |
| 204 | 205 | -1 | volvo 264gl | gas | turbo | four | sedan | rwd | front | 109.1 | ... | 141 | mpfi | 3.78 | 3.15 | 9.5 | 114 | 5400 | 19 | 25 | 22625.0 |
205 rows × 26 columns
#Displaying the first five rows of dataset
df.head()
| car_ID | symboling | CarName | fueltype | aspiration | doornumber | carbody | drivewheel | enginelocation | wheelbase | ... | enginesize | fuelsystem | boreratio | stroke | compressionratio | horsepower | peakrpm | citympg | highwaympg | price | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 3 | alfa-romero giulia | gas | std | two | convertible | rwd | front | 88.6 | ... | 130 | mpfi | 3.47 | 2.68 | 9.0 | 111 | 5000 | 21 | 27 | 13495.0 |
| 1 | 2 | 3 | alfa-romero stelvio | gas | std | two | convertible | rwd | front | 88.6 | ... | 130 | mpfi | 3.47 | 2.68 | 9.0 | 111 | 5000 | 21 | 27 | 16500.0 |
| 2 | 3 | 1 | alfa-romero Quadrifoglio | gas | std | two | hatchback | rwd | front | 94.5 | ... | 152 | mpfi | 2.68 | 3.47 | 9.0 | 154 | 5000 | 19 | 26 | 16500.0 |
| 3 | 4 | 2 | audi 100 ls | gas | std | four | sedan | fwd | front | 99.8 | ... | 109 | mpfi | 3.19 | 3.40 | 10.0 | 102 | 5500 | 24 | 30 | 13950.0 |
| 4 | 5 | 2 | audi 100ls | gas | std | four | sedan | 4wd | front | 99.4 | ... | 136 | mpfi | 3.19 | 3.40 | 8.0 | 115 | 5500 | 18 | 22 | 17450.0 |
5 rows × 26 columns
#Displaying the last five rows of dataset
df.tail()
| car_ID | symboling | CarName | fueltype | aspiration | doornumber | carbody | drivewheel | enginelocation | wheelbase | ... | enginesize | fuelsystem | boreratio | stroke | compressionratio | horsepower | peakrpm | citympg | highwaympg | price | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | 201 | -1 | volvo 145e (sw) | gas | std | four | sedan | rwd | front | 109.1 | ... | 141 | mpfi | 3.78 | 3.15 | 9.5 | 114 | 5400 | 23 | 28 | 16845.0 |
| 201 | 202 | -1 | volvo 144ea | gas | turbo | four | sedan | rwd | front | 109.1 | ... | 141 | mpfi | 3.78 | 3.15 | 8.7 | 160 | 5300 | 19 | 25 | 19045.0 |
| 202 | 203 | -1 | volvo 244dl | gas | std | four | sedan | rwd | front | 109.1 | ... | 173 | mpfi | 3.58 | 2.87 | 8.8 | 134 | 5500 | 18 | 23 | 21485.0 |
| 203 | 204 | -1 | volvo 246 | diesel | turbo | four | sedan | rwd | front | 109.1 | ... | 145 | idi | 3.01 | 3.40 | 23.0 | 106 | 4800 | 26 | 27 | 22470.0 |
| 204 | 205 | -1 | volvo 264gl | gas | turbo | four | sedan | rwd | front | 109.1 | ... | 141 | mpfi | 3.78 | 3.15 | 9.5 | 114 | 5400 | 19 | 25 | 22625.0 |
5 rows × 26 columns
#checking for dupliocates in dataset
df.duplicated
<bound method DataFrame.duplicated of car_ID symboling CarName fueltype aspiration \
0 1 3 alfa-romero giulia gas std
1 2 3 alfa-romero stelvio gas std
2 3 1 alfa-romero Quadrifoglio gas std
3 4 2 audi 100 ls gas std
4 5 2 audi 100ls gas std
.. ... ... ... ... ...
200 201 -1 volvo 145e (sw) gas std
201 202 -1 volvo 144ea gas turbo
202 203 -1 volvo 244dl gas std
203 204 -1 volvo 246 diesel turbo
204 205 -1 volvo 264gl gas turbo
doornumber carbody drivewheel enginelocation wheelbase ... \
0 two convertible rwd front 88.6 ...
1 two convertible rwd front 88.6 ...
2 two hatchback rwd front 94.5 ...
3 four sedan fwd front 99.8 ...
4 four sedan 4wd front 99.4 ...
.. ... ... ... ... ... ...
200 four sedan rwd front 109.1 ...
201 four sedan rwd front 109.1 ...
202 four sedan rwd front 109.1 ...
203 four sedan rwd front 109.1 ...
204 four sedan rwd front 109.1 ...
enginesize fuelsystem boreratio stroke compressionratio horsepower \
0 130 mpfi 3.47 2.68 9.0 111
1 130 mpfi 3.47 2.68 9.0 111
2 152 mpfi 2.68 3.47 9.0 154
3 109 mpfi 3.19 3.40 10.0 102
4 136 mpfi 3.19 3.40 8.0 115
.. ... ... ... ... ... ...
200 141 mpfi 3.78 3.15 9.5 114
201 141 mpfi 3.78 3.15 8.7 160
202 173 mpfi 3.58 2.87 8.8 134
203 145 idi 3.01 3.40 23.0 106
204 141 mpfi 3.78 3.15 9.5 114
peakrpm citympg highwaympg price
0 5000 21 27 13495.0
1 5000 21 27 16500.0
2 5000 19 26 16500.0
3 5500 24 30 13950.0
4 5500 18 22 17450.0
.. ... ... ... ...
200 5400 23 28 16845.0
201 5300 19 25 19045.0
202 5500 18 23 21485.0
203 4800 26 27 22470.0
204 5400 19 25 22625.0
[205 rows x 26 columns]>
#exploring dataset description
df.describe()
| car_ID | symboling | wheelbase | carlength | carwidth | carheight | curbweight | enginesize | boreratio | stroke | compressionratio | horsepower | peakrpm | citympg | highwaympg | price | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 205.000000 | 205.000000 | 205.000000 | 205.000000 | 205.000000 | 205.000000 | 205.000000 | 205.000000 | 205.000000 | 205.000000 | 205.000000 | 205.000000 | 205.000000 | 205.000000 | 205.000000 | 205.000000 |
| mean | 103.000000 | 0.834146 | 98.756585 | 174.049268 | 65.907805 | 53.724878 | 2555.565854 | 126.907317 | 3.329756 | 3.255415 | 10.142537 | 104.117073 | 5125.121951 | 25.219512 | 30.751220 | 13276.710571 |
| std | 59.322565 | 1.245307 | 6.021776 | 12.337289 | 2.145204 | 2.443522 | 520.680204 | 41.642693 | 0.270844 | 0.313597 | 3.972040 | 39.544167 | 476.985643 | 6.542142 | 6.886443 | 7988.852332 |
| min | 1.000000 | -2.000000 | 86.600000 | 141.100000 | 60.300000 | 47.800000 | 1488.000000 | 61.000000 | 2.540000 | 2.070000 | 7.000000 | 48.000000 | 4150.000000 | 13.000000 | 16.000000 | 5118.000000 |
| 25% | 52.000000 | 0.000000 | 94.500000 | 166.300000 | 64.100000 | 52.000000 | 2145.000000 | 97.000000 | 3.150000 | 3.110000 | 8.600000 | 70.000000 | 4800.000000 | 19.000000 | 25.000000 | 7788.000000 |
| 50% | 103.000000 | 1.000000 | 97.000000 | 173.200000 | 65.500000 | 54.100000 | 2414.000000 | 120.000000 | 3.310000 | 3.290000 | 9.000000 | 95.000000 | 5200.000000 | 24.000000 | 30.000000 | 10295.000000 |
| 75% | 154.000000 | 2.000000 | 102.400000 | 183.100000 | 66.900000 | 55.500000 | 2935.000000 | 141.000000 | 3.580000 | 3.410000 | 9.400000 | 116.000000 | 5500.000000 | 30.000000 | 34.000000 | 16503.000000 |
| max | 205.000000 | 3.000000 | 120.900000 | 208.100000 | 72.300000 | 59.800000 | 4066.000000 | 326.000000 | 3.940000 | 4.170000 | 23.000000 | 288.000000 | 6600.000000 | 49.000000 | 54.000000 | 45400.000000 |
df.info
<bound method DataFrame.info of car_ID symboling CarName fueltype aspiration \
0 1 3 alfa-romero giulia gas std
1 2 3 alfa-romero stelvio gas std
2 3 1 alfa-romero Quadrifoglio gas std
3 4 2 audi 100 ls gas std
4 5 2 audi 100ls gas std
.. ... ... ... ... ...
200 201 -1 volvo 145e (sw) gas std
201 202 -1 volvo 144ea gas turbo
202 203 -1 volvo 244dl gas std
203 204 -1 volvo 246 diesel turbo
204 205 -1 volvo 264gl gas turbo
doornumber carbody drivewheel enginelocation wheelbase ... \
0 two convertible rwd front 88.6 ...
1 two convertible rwd front 88.6 ...
2 two hatchback rwd front 94.5 ...
3 four sedan fwd front 99.8 ...
4 four sedan 4wd front 99.4 ...
.. ... ... ... ... ... ...
200 four sedan rwd front 109.1 ...
201 four sedan rwd front 109.1 ...
202 four sedan rwd front 109.1 ...
203 four sedan rwd front 109.1 ...
204 four sedan rwd front 109.1 ...
enginesize fuelsystem boreratio stroke compressionratio horsepower \
0 130 mpfi 3.47 2.68 9.0 111
1 130 mpfi 3.47 2.68 9.0 111
2 152 mpfi 2.68 3.47 9.0 154
3 109 mpfi 3.19 3.40 10.0 102
4 136 mpfi 3.19 3.40 8.0 115
.. ... ... ... ... ... ...
200 141 mpfi 3.78 3.15 9.5 114
201 141 mpfi 3.78 3.15 8.7 160
202 173 mpfi 3.58 2.87 8.8 134
203 145 idi 3.01 3.40 23.0 106
204 141 mpfi 3.78 3.15 9.5 114
peakrpm citympg highwaympg price
0 5000 21 27 13495.0
1 5000 21 27 16500.0
2 5000 19 26 16500.0
3 5500 24 30 13950.0
4 5500 18 22 17450.0
.. ... ... ... ...
200 5400 23 28 16845.0
201 5300 19 25 19045.0
202 5500 18 23 21485.0
203 4800 26 27 22470.0
204 5400 19 25 22625.0
[205 rows x 26 columns]>
#checking for number of duplicates
df.duplicated().sum()
0
#checking of shape of data
df.shape
(205, 26)
Dataset has 205 rows and 26 columns
#displaying the 26 columns
df.columns
Index(['car_ID', 'symboling', 'CarName', 'fueltype', 'aspiration',
'doornumber', 'carbody', 'drivewheel', 'enginelocation', 'wheelbase',
'carlength', 'carwidth', 'carheight', 'curbweight', 'enginetype',
'cylindernumber', 'enginesize', 'fuelsystem', 'boreratio', 'stroke',
'compressionratio', 'horsepower', 'peakrpm', 'citympg', 'highwaympg',
'price'],
dtype='object')
#showing symboling range
df.symboling.unique()
array([ 3, 1, 2, 0, -1, -2], dtype=int64)
#carnames
df.CarName.unique()
array(['alfa-romero giulia', 'alfa-romero stelvio',
'alfa-romero Quadrifoglio', 'audi 100 ls', 'audi 100ls',
'audi fox', 'audi 5000', 'audi 4000', 'audi 5000s (diesel)',
'bmw 320i', 'bmw x1', 'bmw x3', 'bmw z4', 'bmw x4', 'bmw x5',
'chevrolet impala', 'chevrolet monte carlo', 'chevrolet vega 2300',
'dodge rampage', 'dodge challenger se', 'dodge d200',
'dodge monaco (sw)', 'dodge colt hardtop', 'dodge colt (sw)',
'dodge coronet custom', 'dodge dart custom',
'dodge coronet custom (sw)', 'honda civic', 'honda civic cvcc',
'honda accord cvcc', 'honda accord lx', 'honda civic 1500 gl',
'honda accord', 'honda civic 1300', 'honda prelude',
'honda civic (auto)', 'isuzu MU-X', 'isuzu D-Max ',
'isuzu D-Max V-Cross', 'jaguar xj', 'jaguar xf', 'jaguar xk',
'maxda rx3', 'maxda glc deluxe', 'mazda rx2 coupe', 'mazda rx-4',
'mazda glc deluxe', 'mazda 626', 'mazda glc', 'mazda rx-7 gs',
'mazda glc 4', 'mazda glc custom l', 'mazda glc custom',
'buick electra 225 custom', 'buick century luxus (sw)',
'buick century', 'buick skyhawk', 'buick opel isuzu deluxe',
'buick skylark', 'buick century special',
'buick regal sport coupe (turbo)', 'mercury cougar',
'mitsubishi mirage', 'mitsubishi lancer', 'mitsubishi outlander',
'mitsubishi g4', 'mitsubishi mirage g4', 'mitsubishi montero',
'mitsubishi pajero', 'Nissan versa', 'nissan gt-r', 'nissan rogue',
'nissan latio', 'nissan titan', 'nissan leaf', 'nissan juke',
'nissan note', 'nissan clipper', 'nissan nv200', 'nissan dayz',
'nissan fuga', 'nissan otti', 'nissan teana', 'nissan kicks',
'peugeot 504', 'peugeot 304', 'peugeot 504 (sw)', 'peugeot 604sl',
'peugeot 505s turbo diesel', 'plymouth fury iii',
'plymouth cricket', 'plymouth satellite custom (sw)',
'plymouth fury gran sedan', 'plymouth valiant', 'plymouth duster',
'porsche macan', 'porcshce panamera', 'porsche cayenne',
'porsche boxter', 'renault 12tl', 'renault 5 gtl', 'saab 99e',
'saab 99le', 'saab 99gle', 'subaru', 'subaru dl', 'subaru brz',
'subaru baja', 'subaru r1', 'subaru r2', 'subaru trezia',
'subaru tribeca', 'toyota corona mark ii', 'toyota corona',
'toyota corolla 1200', 'toyota corona hardtop',
'toyota corolla 1600 (sw)', 'toyota carina', 'toyota mark ii',
'toyota corolla', 'toyota corolla liftback',
'toyota celica gt liftback', 'toyota corolla tercel',
'toyota corona liftback', 'toyota starlet', 'toyota tercel',
'toyota cressida', 'toyota celica gt', 'toyouta tercel',
'vokswagen rabbit', 'volkswagen 1131 deluxe sedan',
'volkswagen model 111', 'volkswagen type 3', 'volkswagen 411 (sw)',
'volkswagen super beetle', 'volkswagen dasher', 'vw dasher',
'vw rabbit', 'volkswagen rabbit', 'volkswagen rabbit custom',
'volvo 145e (sw)', 'volvo 144ea', 'volvo 244dl', 'volvo 245',
'volvo 264gl', 'volvo diesel', 'volvo 246'], dtype=object)
#engine types
df['enginetype'].unique()
array(['dohc', 'ohcv', 'ohc', 'l', 'rotor', 'ohcf', 'dohcv'], dtype=object)
#fuelsystem types
df['fuelsystem'].unique()
array(['mpfi', '2bbl', 'mfi', '1bbl', 'spfi', '4bbl', 'idi', 'spdi'],
dtype=object)
#fueltypes
df['fueltype'].unique()
array(['gas', 'diesel'], dtype=object)
fuel is of two types, gas and diesel
#pairplot of entire dataset
sns.pairplot(df,palette='coolwarm' )
<seaborn.axisgrid.PairGrid at 0x186a5650be0>
#dataset correlations
df.corr()
| car_ID | symboling | wheelbase | carlength | carwidth | carheight | curbweight | enginesize | boreratio | stroke | compressionratio | horsepower | peakrpm | citympg | highwaympg | price | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| car_ID | 1.000000 | -0.151621 | 0.129729 | 0.170636 | 0.052387 | 0.255960 | 0.071962 | -0.033930 | 0.260064 | -0.160824 | 0.150276 | -0.015006 | -0.203789 | 0.015940 | 0.011255 | -0.109093 |
| symboling | -0.151621 | 1.000000 | -0.531954 | -0.357612 | -0.232919 | -0.541038 | -0.227691 | -0.105790 | -0.130051 | -0.008735 | -0.178515 | 0.070873 | 0.273606 | -0.035823 | 0.034606 | -0.079978 |
| wheelbase | 0.129729 | -0.531954 | 1.000000 | 0.874587 | 0.795144 | 0.589435 | 0.776386 | 0.569329 | 0.488750 | 0.160959 | 0.249786 | 0.353294 | -0.360469 | -0.470414 | -0.544082 | 0.577816 |
| carlength | 0.170636 | -0.357612 | 0.874587 | 1.000000 | 0.841118 | 0.491029 | 0.877728 | 0.683360 | 0.606454 | 0.129533 | 0.158414 | 0.552623 | -0.287242 | -0.670909 | -0.704662 | 0.682920 |
| carwidth | 0.052387 | -0.232919 | 0.795144 | 0.841118 | 1.000000 | 0.279210 | 0.867032 | 0.735433 | 0.559150 | 0.182942 | 0.181129 | 0.640732 | -0.220012 | -0.642704 | -0.677218 | 0.759325 |
| carheight | 0.255960 | -0.541038 | 0.589435 | 0.491029 | 0.279210 | 1.000000 | 0.295572 | 0.067149 | 0.171071 | -0.055307 | 0.261214 | -0.108802 | -0.320411 | -0.048640 | -0.107358 | 0.119336 |
| curbweight | 0.071962 | -0.227691 | 0.776386 | 0.877728 | 0.867032 | 0.295572 | 1.000000 | 0.850594 | 0.648480 | 0.168790 | 0.151362 | 0.750739 | -0.266243 | -0.757414 | -0.797465 | 0.835305 |
| enginesize | -0.033930 | -0.105790 | 0.569329 | 0.683360 | 0.735433 | 0.067149 | 0.850594 | 1.000000 | 0.583774 | 0.203129 | 0.028971 | 0.809769 | -0.244660 | -0.653658 | -0.677470 | 0.874145 |
| boreratio | 0.260064 | -0.130051 | 0.488750 | 0.606454 | 0.559150 | 0.171071 | 0.648480 | 0.583774 | 1.000000 | -0.055909 | 0.005197 | 0.573677 | -0.254976 | -0.584532 | -0.587012 | 0.553173 |
| stroke | -0.160824 | -0.008735 | 0.160959 | 0.129533 | 0.182942 | -0.055307 | 0.168790 | 0.203129 | -0.055909 | 1.000000 | 0.186110 | 0.080940 | -0.067964 | -0.042145 | -0.043931 | 0.079443 |
| compressionratio | 0.150276 | -0.178515 | 0.249786 | 0.158414 | 0.181129 | 0.261214 | 0.151362 | 0.028971 | 0.005197 | 0.186110 | 1.000000 | -0.204326 | -0.435741 | 0.324701 | 0.265201 | 0.067984 |
| horsepower | -0.015006 | 0.070873 | 0.353294 | 0.552623 | 0.640732 | -0.108802 | 0.750739 | 0.809769 | 0.573677 | 0.080940 | -0.204326 | 1.000000 | 0.131073 | -0.801456 | -0.770544 | 0.808139 |
| peakrpm | -0.203789 | 0.273606 | -0.360469 | -0.287242 | -0.220012 | -0.320411 | -0.266243 | -0.244660 | -0.254976 | -0.067964 | -0.435741 | 0.131073 | 1.000000 | -0.113544 | -0.054275 | -0.085267 |
| citympg | 0.015940 | -0.035823 | -0.470414 | -0.670909 | -0.642704 | -0.048640 | -0.757414 | -0.653658 | -0.584532 | -0.042145 | 0.324701 | -0.801456 | -0.113544 | 1.000000 | 0.971337 | -0.685751 |
| highwaympg | 0.011255 | 0.034606 | -0.544082 | -0.704662 | -0.677218 | -0.107358 | -0.797465 | -0.677470 | -0.587012 | -0.043931 | 0.265201 | -0.770544 | -0.054275 | 0.971337 | 1.000000 | -0.697599 |
| price | -0.109093 | -0.079978 | 0.577816 | 0.682920 | 0.759325 | 0.119336 | 0.835305 | 0.874145 | 0.553173 | 0.079443 | 0.067984 | 0.808139 | -0.085267 | -0.685751 | -0.697599 | 1.000000 |
#dataset correlation table
plt.figure(figsize=(20,12))
sns.heatmap(df.corr(), annot=True, cmap="magma",)
plt.show()
sns.histplot(x='carheight',data=df, bins=10);
df.isnull().sum()
car_ID 0 symboling 0 CarName 0 fueltype 0 aspiration 0 doornumber 0 carbody 0 drivewheel 0 enginelocation 0 wheelbase 0 carlength 0 carwidth 0 carheight 0 curbweight 0 enginetype 0 cylindernumber 0 enginesize 0 fuelsystem 0 boreratio 0 stroke 0 compressionratio 0 horsepower 0 peakrpm 0 citympg 0 highwaympg 0 price 0 dtype: int64
abs(df.corr().price).sort_values(ascending=False)
price 1.000000 enginesize 0.874145 curbweight 0.835305 horsepower 0.808139 carwidth 0.759325 highwaympg 0.697599 citympg 0.685751 carlength 0.682920 wheelbase 0.577816 boreratio 0.553173 carheight 0.119336 car_ID 0.109093 peakrpm 0.085267 symboling 0.079978 stroke 0.079443 compressionratio 0.067984 Name: price, dtype: float64
report=ProfileReport(df)
report
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